class_docstr: Class for the manifold of geometric distributions.
  
  This is the parameter space of geometric distributions
  i.e. [0,1] segment.
docstr: Sample parameters of geometric distributions.
  
  The uniform distribution on (0, 1) is used.
  
  Parameters
  ----------
  n_samples : int
      Number of samples.
      Optional, default: 1.
  bound : float
      Right-end ot the segment where geometric parameters are sampled.
      Optional, default: 1.
  
  Returns
  -------
  samples : array-like, shape=[n_samples,]
      Sample of points representing geometric distributions.
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code: def random_point(self, n_samples=1):
      """Sample parameters of geometric distributions.
  
      The uniform distribution on (0, 1) is used.
  
      Parameters
      ----------
      n_samples : int
          Number of samples.
          Optional, default: 1.
      bound : float
          Right-end ot the segment where geometric parameters are sampled.
          Optional, default: 1.
  
      Returns
      -------
      samples : array-like, shape=[n_samples,]
          Sample of points representing geometric distributions.
      """
      size = (n_samples, self.dim) if n_samples != 1 else (self.dim,)
      return gs.random.rand(*size)
  
imports: from scipy.stats import geom
  import geomstats.backend
  from geomstats.geometry.base import VectorSpaceOpenSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.information_geometry.base import InformationManifoldMixin, ScipyUnivariateRandomVariable
label_desc: Use image node_id 4 for calling the GeometricDistributions obj's underlying member method code with example usage: obj.random_point(n_samples) and returns: gs